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Artigo de periodico
Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data (2025)
Silva, Gabriel Ferreira dos Santos ; Wichmann, Roberta Moreira ; Silva Junior, Francisco Costa da; Chiavegatto Filho, Alexandre Dias Porto
Source: Scientific Reports. Unidade: FSP
Subjects: MORTALIDADE NEONATAL, APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL, SAÚDE DA CRIANÇA, RISCO, DESIGUALDADES EM SAÚDE
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ABNT
SILVA, Gabriel Ferreira dos Santos et al. Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data. Scientific Reports, v. 15, 2025Tradução . . Disponível em: https://repositorio.usp.br/directbitstream/b41d7373-ed8c-4b99-92b4-947c72362000/HEP_16_2025.pdf. Acesso em: 27 nov. 2025.
APA
Silva, G. F. dos S., Wichmann, R. M., Silva Junior, F. C. da, & Chiavegatto Filho, A. D. P. (2025). Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data. Scientific Reports, 15. doi:10.1038/s41598-025-04066-5
NLM
Silva GF dos S, Wichmann RM, Silva Junior FC da, Chiavegatto Filho ADP. Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data [Internet]. Scientific Reports. 2025 ;15[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/b41d7373-ed8c-4b99-92b4-947c72362000/HEP_16_2025.pdf
Vancouver
Silva GF dos S, Wichmann RM, Silva Junior FC da, Chiavegatto Filho ADP. Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data [Internet]. Scientific Reports. 2025 ;15[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/b41d7373-ed8c-4b99-92b4-947c72362000/HEP_16_2025.pdf
Artigo de periodico
Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data (2025)
Ciba, Manuel ; Petzold, Marc ; Alves, Caroline Lourenço ; Rodrigues, Francisco Aparecido ; Jimbo, Yasuhiko ; Thielemann, Christiane
Source: Scientific Reports. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS, NEUROFARMACOLOGIA
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CIBA, Manuel et al. Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data. Scientific Reports, v. 15, p. 1-14, 2025Tradução . . Disponível em: https://doi.org/10.1038/s41598-025-99479-7. Acesso em: 27 nov. 2025.
APA
Ciba, M., Petzold, M., Alves, C. L., Rodrigues, F. A., Jimbo, Y., & Thielemann, C. (2025). Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data. Scientific Reports, 15, 1-14. doi:10.1038/s41598-025-99479-7
NLM
Ciba M, Petzold M, Alves CL, Rodrigues FA, Jimbo Y, Thielemann C. Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data [Internet]. Scientific Reports. 2025 ; 15 1-14.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-025-99479-7
Vancouver
Ciba M, Petzold M, Alves CL, Rodrigues FA, Jimbo Y, Thielemann C. Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data [Internet]. Scientific Reports. 2025 ; 15 1-14.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-025-99479-7
Artigo de periodico
Comparative analysis of traditional and Gaussian Analytical Hierarchy Process (AHP) methods for landslide susceptibility assessment (2025)
Marques-Carvalho, Rômulo ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Escobar-Silva, Elton Vicente ; Quevedo, Renata Pacheco ; Almeida, Cláudia Maria de ; Santos, Marcos
Source: Scientific Reports. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), DESASTRES AMBIENTAIS, DESLIZAMENTO DE TERRA
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MARQUES-CARVALHO, Rômulo et al. Comparative analysis of traditional and Gaussian Analytical Hierarchy Process (AHP) methods for landslide susceptibility assessment. Scientific Reports, v. 15, p. 1-21, 2025Tradução . . Disponível em: https://doi.org/10.1038/s41598-025-22136-6. Acesso em: 27 nov. 2025.
APA
Marques-Carvalho, R., Carvalho, A. C. P. de L. F. de, Escobar-Silva, E. V., Quevedo, R. P., Almeida, C. M. de, & Santos, M. (2025). Comparative analysis of traditional and Gaussian Analytical Hierarchy Process (AHP) methods for landslide susceptibility assessment. Scientific Reports, 15, 1-21. doi:10.1038/s41598-025-22136-6
NLM
Marques-Carvalho R, Carvalho ACP de LF de, Escobar-Silva EV, Quevedo RP, Almeida CM de, Santos M. Comparative analysis of traditional and Gaussian Analytical Hierarchy Process (AHP) methods for landslide susceptibility assessment [Internet]. Scientific Reports. 2025 ; 15 1-21.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-025-22136-6
Vancouver
Marques-Carvalho R, Carvalho ACP de LF de, Escobar-Silva EV, Quevedo RP, Almeida CM de, Santos M. Comparative analysis of traditional and Gaussian Analytical Hierarchy Process (AHP) methods for landslide susceptibility assessment [Internet]. Scientific Reports. 2025 ; 15 1-21.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-025-22136-6
Artigo de periodico
Evaluating YOLO architectures for detecting road killed endangered Brazilian animals (2024)
Ferrante, Gabriel Souto ; Nakamura, Luis Hideo Vasconcelos ; Sampaio, Sandra Coccuzzo ; Rocha Filho, Geraldo Pereira ; Meneguette, Rodolfo Ipolito
Source: Scientific Reports. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, VISÃO COMPUTACIONAL, ANIMAIS SELVAGENS, ATROPELAMENTO
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FERRANTE, Gabriel Souto et al. Evaluating YOLO architectures for detecting road killed endangered Brazilian animals. Scientific Reports, v. 14, p. 1-17, 2024Tradução . . Disponível em: https://doi.org/10.1038/s41598-024-52054-y. Acesso em: 27 nov. 2025.
APA
Ferrante, G. S., Nakamura, L. H. V., Sampaio, S. C., Rocha Filho, G. P., & Meneguette, R. I. (2024). Evaluating YOLO architectures for detecting road killed endangered Brazilian animals. Scientific Reports, 14, 1-17. doi:10.1038/s41598-024-52054-y
NLM
Ferrante GS, Nakamura LHV, Sampaio SC, Rocha Filho GP, Meneguette RI. Evaluating YOLO architectures for detecting road killed endangered Brazilian animals [Internet]. Scientific Reports. 2024 ; 14 1-17.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-024-52054-y
Vancouver
Ferrante GS, Nakamura LHV, Sampaio SC, Rocha Filho GP, Meneguette RI. Evaluating YOLO architectures for detecting road killed endangered Brazilian animals [Internet]. Scientific Reports. 2024 ; 14 1-17.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-024-52054-y
Artigo de periodico
Reducing delivery insurance costs through risk score model for food delivery company (2024)
Panham, Diogo Silva; Louzada, Francisco ; Ramos, Pedro Luiz
Source: Scientific Reports. Unidades: ICMC, Interinstitucional de Pós-Graduação em Estatística
Subjects: POLÍTICA DE PREÇO, LOGÍSTICA, APRENDIZADO COMPUTACIONAL, SEGUROS
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PANHAM, Diogo Silva e LOUZADA, Francisco e RAMOS, Pedro Luiz. Reducing delivery insurance costs through risk score model for food delivery company. Scientific Reports, v. 14, p. 1-14, 2024Tradução . . Disponível em: https://doi.org/10.1038/s41598-024-57548-3. Acesso em: 27 nov. 2025.
APA
Panham, D. S., Louzada, F., & Ramos, P. L. (2024). Reducing delivery insurance costs through risk score model for food delivery company. Scientific Reports, 14, 1-14. doi:10.1038/s41598-024-57548-3
NLM
Panham DS, Louzada F, Ramos PL. Reducing delivery insurance costs through risk score model for food delivery company [Internet]. Scientific Reports. 2024 ; 14 1-14.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-024-57548-3
Vancouver
Panham DS, Louzada F, Ramos PL. Reducing delivery insurance costs through risk score model for food delivery company [Internet]. Scientific Reports. 2024 ; 14 1-14.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-024-57548-3
Artigo de periodico
Machine learning for predicting survival of colorectal cancer patients (2023)
Cardoso, Lucas Buk; Parro, Vanderlei Cunha; Peres, Stela Verzinhasse ; Curado, Maria Paula ; Fernandes, Gisele Aparecida ; Wünsch Filho, Victor ; Toporcov, Tatiana Natasha
Source: Scientific Reports. Unidade: FSP
Subjects: NEOPLASIAS COLORRETAIS, APRENDIZADO COMPUTACIONAL, TAXA DE SOBREVIVÊNCIA, REGISTROS HOSPITALARES, INCIDÊNCIA, SÃO PAULO
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ABNT
CARDOSO, Lucas Buk et al. Machine learning for predicting survival of colorectal cancer patients. Scientific Reports, v. 13, p. art. 8874 [9], 2023Tradução . . Disponível em: https://doi.org/10.1038/s41598-023-35649-9. Acesso em: 27 nov. 2025.
APA
Cardoso, L. B., Parro, V. C., Peres, S. V., Curado, M. P., Fernandes, G. A., Wünsch Filho, V., & Toporcov, T. N. (2023). Machine learning for predicting survival of colorectal cancer patients. Scientific Reports, 13, art. 8874 [9]. doi:10.1038/s41598-023-35649-9
NLM
Cardoso LB, Parro VC, Peres SV, Curado MP, Fernandes GA, Wünsch Filho V, Toporcov TN. Machine learning for predicting survival of colorectal cancer patients [Internet]. Scientific Reports. 2023 ;13 art. 8874 [9].[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-023-35649-9
Vancouver
Cardoso LB, Parro VC, Peres SV, Curado MP, Fernandes GA, Wünsch Filho V, Toporcov TN. Machine learning for predicting survival of colorectal cancer patients [Internet]. Scientific Reports. 2023 ;13 art. 8874 [9].[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-023-35649-9
Artigo de periodico
Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts (2023)
Wichmann, Roberta Moreira ; Fernandes, Fernando Timoteo ; Chiavegatto Filho, Alexandre Dias Porto
Source: Scientific Reports. Unidade: FSP
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS, COVID-19, HOSPITAIS, ESTUDOS DE COORTES
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WICHMANN, Roberta Moreira e FERNANDES, Fernando Timoteo e CHIAVEGATTO FILHO, Alexandre Dias Porto. Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts. Scientific Reports, v. 13, p. art. 1022 [8], 2023Tradução . . Disponível em: https://doi.org/10.1038/s41598-022-26467-6. Acesso em: 27 nov. 2025.
APA
Wichmann, R. M., Fernandes, F. T., & Chiavegatto Filho, A. D. P. (2023). Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts. Scientific Reports, 13, art. 1022 [8]. doi:10.1038/s41598-022-26467-6
NLM
Wichmann RM, Fernandes FT, Chiavegatto Filho ADP. Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts [Internet]. Scientific Reports. 2023 ;13 art. 1022 [8].[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-022-26467-6
Vancouver
Wichmann RM, Fernandes FT, Chiavegatto Filho ADP. Improving the performance of machine learning algorithms for health outcomes predictions in multicentric cohorts [Internet]. Scientific Reports. 2023 ;13 art. 1022 [8].[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-022-26467-6
Artigo de periodico
A joint learning approach for genomic prediction in polyploid grasses (2022)
Aono, Alexandre Hild; Ferreira, Rebecca Caroline Ulbricht; Moraes, Aline da Costa Lima; Lara, Letícia Aparecida de Castro ; Pimenta, Ricardo José Gonzaga; Costa, Estela Araujo; Pinto, Luciana Rossini; Landell, Marcos Guimarães de Andrade; Santos, Mateus Figueiredo; Jank, Liana; Barrios, Sanzio Carvalho Lima; Valle, Cacilda Borges do; Chiari, Lucimara; Garcia, Antonio Augusto Franco ; Kuroshu, Reginaldo Massanobu; Lorena, Ana Carolina; Gorjanc, Gregor; Souza, Anete Pereira de
Source: Scientific Reports. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CANA-DE-AÇÚCAR, CAPIM BRAQUIÁRIA, CAPIM MOMBAÇA, CROMOSSOMOS VEGETAIS, GENÔMICA, MELHORAMENTO GENÉTICO VEGETAL, SELEÇÃO GENÉTICA
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AONO, Alexandre Hild et al. A joint learning approach for genomic prediction in polyploid grasses. Scientific Reports, v. 12, p. 1-17, 2022Tradução . . Disponível em: https://doi.org/10.1038/s41598-022-16417-7. Acesso em: 27 nov. 2025.
APA
Aono, A. H., Ferreira, R. C. U., Moraes, A. da C. L., Lara, L. A. de C., Pimenta, R. J. G., Costa, E. A., et al. (2022). A joint learning approach for genomic prediction in polyploid grasses. Scientific Reports, 12, 1-17. doi:10.1038/s41598-022-16417-7
NLM
Aono AH, Ferreira RCU, Moraes A da CL, Lara LA de C, Pimenta RJG, Costa EA, Pinto LR, Landell MG de A, Santos MF, Jank L, Barrios SCL, Valle CB do, Chiari L, Garcia AAF, Kuroshu RM, Lorena AC, Gorjanc G, Souza AP de. A joint learning approach for genomic prediction in polyploid grasses [Internet]. Scientific Reports. 2022 ; 12 1-17.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-022-16417-7
Vancouver
Aono AH, Ferreira RCU, Moraes A da CL, Lara LA de C, Pimenta RJG, Costa EA, Pinto LR, Landell MG de A, Santos MF, Jank L, Barrios SCL, Valle CB do, Chiari L, Garcia AAF, Kuroshu RM, Lorena AC, Gorjanc G, Souza AP de. A joint learning approach for genomic prediction in polyploid grasses [Internet]. Scientific Reports. 2022 ; 12 1-17.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-022-16417-7
Artigo de periodico
A divide‑and‑conquer approach for genomic prediction in rubber tree using machine learning (2022)
Aono, Alexandre Hild; Francisco, Felipe Roberto; Souza, Livia Moura; Gonçalves, Paulo de Souza; Scaloppi Junior, Erivaldo J; Le Guen, Vincent; Fritsche‑Neto, Roberto ; Gorjanc, Gregor; Marcos Gonçalves Quiles8; Souza, Anete Pereira de
Source: Scientific Reports. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, GENÔMICA, MARCADOR MOLECULAR, MELHORAMENTO GENÉTICO VEGETAL, REDES NEURAIS, SERINGUEIRA
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AONO, Alexandre Hild et al. A divide‑and‑conquer approach for genomic prediction in rubber tree using machine learning. Scientific Reports, v. 12, p. 1-14, 2022Tradução . . Disponível em: https://repositorio.usp.br/directbitstream/86d8ec26-96c6-424e-a56e-717547051442/3105258-A%20divide-and-conquer%20approach%20for%20genomic%20prediction%20in%20rubber%20tree%20using%20machine%20learning.pdf. Acesso em: 27 nov. 2025.
APA
Aono, A. H., Francisco, F. R., Souza, L. M., Gonçalves, P. de S., Scaloppi Junior, E. J., Le Guen, V., et al. (2022). A divide‑and‑conquer approach for genomic prediction in rubber tree using machine learning. Scientific Reports, 12, 1-14. doi:10.1038/s41598-022-20416-z
NLM
Aono AH, Francisco FR, Souza LM, Gonçalves P de S, Scaloppi Junior EJ, Le Guen V, Fritsche‑Neto R, Gorjanc G, Marcos Gonçalves Quiles8, Souza AP de. A divide‑and‑conquer approach for genomic prediction in rubber tree using machine learning [Internet]. Scientific Reports. 2022 ; 12 1-14.[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/86d8ec26-96c6-424e-a56e-717547051442/3105258-A%20divide-and-conquer%20approach%20for%20genomic%20prediction%20in%20rubber%20tree%20using%20machine%20learning.pdf
Vancouver
Aono AH, Francisco FR, Souza LM, Gonçalves P de S, Scaloppi Junior EJ, Le Guen V, Fritsche‑Neto R, Gorjanc G, Marcos Gonçalves Quiles8, Souza AP de. A divide‑and‑conquer approach for genomic prediction in rubber tree using machine learning [Internet]. Scientific Reports. 2022 ; 12 1-14.[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/86d8ec26-96c6-424e-a56e-717547051442/3105258-A%20divide-and-conquer%20approach%20for%20genomic%20prediction%20in%20rubber%20tree%20using%20machine%20learning.pdf
Artigo de periodico
Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII (2021)
Lopes, Tiago José da Silva ; Rios, Ricardo Araújo ; Nogueira, Tatiane ; Mello, Rodrigo Fernandes de
Source: Scientific Reports. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA, HEMOFILIA, PROTEÍNAS
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ABNT
LOPES, Tiago José da Silva et al. Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII. Scientific Reports, v. 11, p. 1-11, 2021Tradução . . Disponível em: https://doi.org/10.1038/s41598-021-92201-3. Acesso em: 27 nov. 2025.
APA
Lopes, T. J. da S., Rios, R. A., Nogueira, T., & Mello, R. F. de. (2021). Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII. Scientific Reports, 11, 1-11. doi:10.1038/s41598-021-92201-3
NLM
Lopes TJ da S, Rios RA, Nogueira T, Mello RF de. Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII [Internet]. Scientific Reports. 2021 ; 11 1-11.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-021-92201-3
Vancouver
Lopes TJ da S, Rios RA, Nogueira T, Mello RF de. Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII [Internet]. Scientific Reports. 2021 ; 11 1-11.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-021-92201-3
Artigo de periodico
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality (2021)
Silva, Clíssia Barboza da ; Oliveira, Nielsen Moreira ; Carvalho, Marcia Eugenia Amaral de ; Medeiros, André Dantas de; Nogueira, Marina de Lima; Reis, André Rodrigues dos
Source: Scientific Reports. Unidades: CENA, ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA ÓPTICA, GERMINAÇÃO DE SEMENTES, PROCESSAMENTO DE IMAGENS, SEMENTES, SOJA
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SILVA, Clíssia Barboza da et al. Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality. Scientific Reports, v. 11, p. 1-12, 2021Tradução . . Disponível em: https://doi.org/10.1038/s41598-021-97223-5. Acesso em: 27 nov. 2025.
APA
Silva, C. B. da, Oliveira, N. M., Carvalho, M. E. A. de, Medeiros, A. D. de, Nogueira, M. de L., & Reis, A. R. dos. (2021). Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality. Scientific Reports, 11, 1-12. doi:10.1038/s41598-021-97223-5
NLM
Silva CB da, Oliveira NM, Carvalho MEA de, Medeiros AD de, Nogueira M de L, Reis AR dos. Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality [Internet]. Scientific Reports. 2021 ; 11 1-12.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-021-97223-5
Vancouver
Silva CB da, Oliveira NM, Carvalho MEA de, Medeiros AD de, Nogueira M de L, Reis AR dos. Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality [Internet]. Scientific Reports. 2021 ; 11 1-12.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-021-97223-5
Artigo de periodico
Interactive machine learning for soybean seed and seedling quality classification (2020)
Medeiros, André Dantas de; Capobiango, Nayara Pereira; Silva, José Maria da; Silva, Laércio Junio da; Silva, Clíssia Barboza da ; Santos Dias, Denise Cunha Fernandes dos
Source: Scientific Reports. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, FISIOLOGIA VEGETAL, MUDAS, SEMENTES, SOJA
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ABNT
MEDEIROS, André Dantas de et al. Interactive machine learning for soybean seed and seedling quality classification. Scientific Reports, v. 10, p. 1-10, 2020Tradução . . Disponível em: https://doi.org/10.1038/s41598-020-68273-y. Acesso em: 27 nov. 2025.
APA
Medeiros, A. D. de, Capobiango, N. P., Silva, J. M. da, Silva, L. J. da, Silva, C. B. da, & Santos Dias, D. C. F. dos. (2020). Interactive machine learning for soybean seed and seedling quality classification. Scientific Reports, 10, 1-10. doi:10.1038/s41598-020-68273-y
NLM
Medeiros AD de, Capobiango NP, Silva JM da, Silva LJ da, Silva CB da, Santos Dias DCF dos. Interactive machine learning for soybean seed and seedling quality classification [Internet]. Scientific Reports. 2020 ; 10 1-10.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-020-68273-y
Vancouver
Medeiros AD de, Capobiango NP, Silva JM da, Silva LJ da, Silva CB da, Santos Dias DCF dos. Interactive machine learning for soybean seed and seedling quality classification [Internet]. Scientific Reports. 2020 ; 10 1-10.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-020-68273-y
Artigo de periodico
Reconstructing commuters network using machine learning and urban indicators (2019)
Spadon, Gabriel ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Rodrigues Junior, José Fernando ; Alves, Luiz Gustavo de Andrade
Source: Scientific Reports. Unidade: ICMC
Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, MOBILIDADE URBANA, TRANSPORTE INTERURBANO
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ABNT
SPADON, Gabriel et al. Reconstructing commuters network using machine learning and urban indicators. Scientific Reports, v. 9, p. 11801-1-11801-13, 2019Tradução . . Disponível em: https://doi.org/10.1038/s41598-019-48295-x. Acesso em: 27 nov. 2025.
APA
Spadon, G., Carvalho, A. C. P. de L. F. de, Rodrigues Junior, J. F., & Alves, L. G. de A. (2019). Reconstructing commuters network using machine learning and urban indicators. Scientific Reports, 9, 11801-1-11801-13. doi:10.1038/s41598-019-48295-x
NLM
Spadon G, Carvalho ACP de LF de, Rodrigues Junior JF, Alves LG de A. Reconstructing commuters network using machine learning and urban indicators [Internet]. Scientific Reports. 2019 ; 9 11801-1-11801-13.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-019-48295-x
Vancouver
Spadon G, Carvalho ACP de LF de, Rodrigues Junior JF, Alves LG de A. Reconstructing commuters network using machine learning and urban indicators [Internet]. Scientific Reports. 2019 ; 9 11801-1-11801-13.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-019-48295-x
Artigo de periodico
Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit (2019)
Kepchia, Devin; Xu, Pingxi; Terryn, Raymond; Castro, Ana; Schürer, Stephan C; Leal, Walter Soares ; Luetje, Charles W
Source: Scientific Reports. Unidade: ESALQ
Subjects: ANOPHELES, ANTAGONISTAS, APRENDIZADO COMPUTACIONAL, COMPOSTOS VOLÁTEIS, DROSOPHILA, MOSQUITOS, OLFATO, RECEPTORES SENSORIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
KEPCHIA, Devin et al. Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit. Scientific Reports, v. 9, p. 1-18, 2019Tradução . . Disponível em: https://doi.org/10.1038/s41598-019-40640-4. Acesso em: 27 nov. 2025.
APA
Kepchia, D., Xu, P., Terryn, R., Castro, A., Schürer, S. C., Leal, W. S., & Luetje, C. W. (2019). Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit. Scientific Reports, 9, 1-18. doi:10.1038/s41598-019-40640-4
NLM
Kepchia D, Xu P, Terryn R, Castro A, Schürer SC, Leal WS, Luetje CW. Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit [Internet]. Scientific Reports. 2019 ; 9 1-18.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-019-40640-4
Vancouver
Kepchia D, Xu P, Terryn R, Castro A, Schürer SC, Leal WS, Luetje CW. Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit [Internet]. Scientific Reports. 2019 ; 9 1-18.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1038/s41598-019-40640-4

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